Each access of a Pandas hdf5 store node is a re-copy from the file

This is obvious, but it is important to remember.

import pandas as pd, pylab, cProfile

def create_file():
  r = pylab.randn(10000,1000)
  p = pd.DataFrame(r)

  with pd.get_store('test.h5','w') as store:
    store['data'] = p

def analyze(p):
  return [(p[c] > 0).size for c in [0,1,2,3,4,5,6,7,8,9]]


def copy1():
  print 'Working on copy of data'
  with pd.get_store('test.h5','r') as store:
    p = store['data']
    idx = analyze(p)
    print idx

def copy2():
  print 'Working on copy of data'
  with pd.get_store('test.h5','r') as store:
    idx = analyze(store['data'])
    print idx

def ref():
  print 'Working on hdf5 store reference'
  with pd.get_store('test.h5','r') as store:
    idx = [(store['data'][c] > 0).size for c in [0,1,2,3,4,5,6,7,8,9]]
    print idx

#create_file()
cProfile.run('copy1()')
cProfile.run('copy1()')
cProfile.run('copy2()')
cProfile.run('ref()')

When run with python test.py | grep "function calls" gives us

         5340 function calls (5256 primitive calls) in 0.094 seconds
         2080 function calls (2040 primitive calls) in 0.048 seconds
         2080 function calls (2040 primitive calls) in 0.050 seconds
         5661 function calls (5621 primitive calls) in 0.402 seconds

So, if you are going to do multiple operations on the data in a node it is better to copy it over once (if you have the memory).